



'''simple docstring'''






import os
import sys

lowerCAmelCase:   Optional[Any]					=		os.path.join(os.path.dirname(__file__), 'src')
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


lowerCAmelCase:   Union[str, Any]					=		[
    'torch',
    'numpy',
    'tokenizers',
    'filelock',
    'requests',
    'tqdm',
    'regex',
    'sentencepiece',
    'sacremoses',
    'importlib_metadata',
    'huggingface_hub',
]
@add_start_docstrings(AutoConfig.__doc__							)
def  lowerCamelCase__			(		*_A					,     **_A							):
  return AutoConfig.from_pretrained(*_A					,     **_A							)
@add_start_docstrings(AutoTokenizer.__doc__							)
def  lowerCamelCase__			(		*_A					,     **_A							):
  return AutoTokenizer.from_pretrained(*_A					,     **_A							)
@add_start_docstrings(AutoModel.__doc__							)
def  lowerCamelCase__			(		*_A					,     **_A							):
  return AutoModel.from_pretrained(*_A					,     **_A							)
@add_start_docstrings(AutoModelForCausalLM.__doc__							)
def  lowerCamelCase__			(		*_A					,     **_A							):
  return AutoModelForCausalLM.from_pretrained(*_A					,     **_A							)
@add_start_docstrings(AutoModelForMaskedLM.__doc__							)
def  lowerCamelCase__			(		*_A					,     **_A							):
  return AutoModelForMaskedLM.from_pretrained(*_A					,     **_A							)
@add_start_docstrings(AutoModelForSequenceClassification.__doc__							)
def  lowerCamelCase__			(		*_A					,     **_A							):
  return AutoModelForSequenceClassification.from_pretrained(*_A					,     **_A							)

@add_start_docstrings(AutoModelForQuestionAnswering.__doc__							)
def  lowerCamelCase__			(		*_A					,     **_A							):
  return AutoModelForQuestionAnswering.from_pretrained(*_A					,     **_A							)




'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCAmelCase:   Dict					=		{'configuration_vit_mae': ['VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMAEConfig']}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase:   Optional[int]					=		[
         'VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST',
         'ViTMAEForPreTraining',
         'ViTMAELayer',
         'ViTMAEModel',
         'ViTMAEPreTrainedModel',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase:   List[Any]					=		[
         'TFViTMAEForPreTraining',
         'TFViTMAEModel',
         'TFViTMAEPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_vit_mae import (
              VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
              ViTMAEForPreTraining,
              ViTMAELayer,
              ViTMAEModel,
              ViTMAEPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
     import sys

     lowerCAmelCase:   Tuple					=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)